Object recognition of a mobile robot based on SIFT with de-speckle filtering

Authors:
Zhiguang Xu;Kyung-Sik Choi;Yanyan Dai;Suk-Gyu Lee
Affiliations:
Department of Electrical Engineering, Yeungnam University, Gyongbuk, Republic of Korea;Department of Electrical Engineering, Yeungnam University, Gyongbuk, Republic of Korea;Department of Electrical Engineering, Yeungnam University, Gyongbuk, Republic of Korea;Department of Electrical Engineering, Yeungnam University, Gyongbuk, Republic of Korea
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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Abstract

This paper presents a novel object recognition method, of a mobile robot, by combining scale invariant feature transform (SIFT) and de-speckle filtering to enhance the recognition capability. The main idea of the proposed algorithm is to use SIFT programming to identify other robots after de-speckle filtering process to remove outside noise. Since a number of features are much larger than needed, SIFT method requires a long time to extract and match the features. The proposed method shows a faster and more efficient performance, which enhances localization accuracy of the slave robots. From the simulation results, the method using de-speckle filtering based SIFT shows that the number of features in the extraction process, and that the points in matching process are reduced.